Overview

Dataset statistics

Number of variables18
Number of observations9682
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory1.7 MiB
Average record size in memory188.5 B

Variable types

Categorical1
Numeric17

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
ExternalRiskEstimate is highly overall correlated with MaxDelq/PublicRecLast12M and 3 other fieldsHigh correlation
MSinceMostRecentTradeOpen is highly overall correlated with NumTradesOpeninLast12MHigh correlation
MaxDelq/PublicRecLast12M is highly overall correlated with ExternalRiskEstimate and 3 other fieldsHigh correlation
MaxDelqEver is highly overall correlated with ExternalRiskEstimate and 4 other fieldsHigh correlation
NetFractionRevolvingBurden is highly overall correlated with ExternalRiskEstimate and 1 other fieldsHigh correlation
NumInqLast6M is highly overall correlated with NumInqLast6Mexcl7daysHigh correlation
NumInqLast6Mexcl7days is highly overall correlated with NumInqLast6MHigh correlation
NumSatisfactoryTrades is highly overall correlated with NumTotalTradesHigh correlation
NumTotalTrades is highly overall correlated with NumSatisfactoryTradesHigh correlation
NumTrades60Ever/DerogPubRec is highly overall correlated with MaxDelq/PublicRecLast12M and 3 other fieldsHigh correlation
NumTrades90Ever/DerogPubRec is highly overall correlated with MaxDelqEver and 1 other fieldsHigh correlation
NumTradesOpeninLast12M is highly overall correlated with MSinceMostRecentTradeOpenHigh correlation
PercentTradesNeverDelq is highly overall correlated with ExternalRiskEstimate and 3 other fieldsHigh correlation
PercentTradesWBalance is highly overall correlated with NetFractionRevolvingBurdenHigh correlation
MSinceMostRecentTradeOpen has 105 (1.1%) zerosZeros
NumTrades60Ever/DerogPubRec has 6706 (69.3%) zerosZeros
NumTrades90Ever/DerogPubRec has 7528 (77.8%) zerosZeros
MaxDelq/PublicRecLast12M has 323 (3.3%) zerosZeros
NumTotalTrades has 131 (1.4%) zerosZeros
NumTradesOpeninLast12M has 2397 (24.8%) zerosZeros
PercentInstallTrades has 186 (1.9%) zerosZeros
NumInqLast6M has 3838 (39.6%) zerosZeros
NumInqLast6Mexcl7days has 3986 (41.2%) zerosZeros
NetFractionRevolvingBurden has 608 (6.3%) zerosZeros
NumRevolvingTradesWBalance has 295 (3.0%) zerosZeros

Reproduction

Analysis started2024-05-31 09:30:38.251235
Analysis finished2024-05-31 09:32:05.852619
Duration1 minute and 27.6 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

RiskPerformance
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size496.4 KiB
Bad
4985 
Good
4697 

Length

Max length4
Median length3
Mean length3.485127
Min length3

Characters and Unicode

Total characters33743
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBad
2nd rowBad
3rd rowBad
4th rowBad
5th rowBad

Common Values

ValueCountFrequency (%)
Bad 4985
51.5%
Good 4697
48.5%

Length

2024-05-31T11:32:06.095139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-31T11:32:06.461156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
bad 4985
51.5%
good 4697
48.5%

Most occurring characters

ValueCountFrequency (%)
d 9682
28.7%
o 9394
27.8%
B 4985
14.8%
a 4985
14.8%
G 4697
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 9682
28.7%
o 9394
27.8%
B 4985
14.8%
a 4985
14.8%
G 4697
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 9682
28.7%
o 9394
27.8%
B 4985
14.8%
a 4985
14.8%
G 4697
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 9682
28.7%
o 9394
27.8%
B 4985
14.8%
a 4985
14.8%
G 4697
13.9%

ExternalRiskEstimate
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.150072
Minimum33
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:06.849119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile57
Q165
median72
Q380
95-th percentile88
Maximum94
Range61
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.8768418
Coefficient of variation (CV)0.13689303
Kurtosis-0.77192456
Mean72.150072
Median Absolute Deviation (MAD)8
Skewness-0.0057167372
Sum698557
Variance97.552003
MonotonicityNot monotonic
2024-05-31T11:32:07.350558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 378
 
3.9%
66 365
 
3.8%
72 362
 
3.7%
68 362
 
3.7%
73 359
 
3.7%
70 344
 
3.6%
63 331
 
3.4%
75 328
 
3.4%
69 306
 
3.2%
80 297
 
3.1%
Other values (49) 6250
64.6%
ValueCountFrequency (%)
33 1
 
< 0.1%
34 1
 
< 0.1%
36 2
< 0.1%
37 1
 
< 0.1%
40 2
< 0.1%
41 1
 
< 0.1%
42 2
< 0.1%
43 4
< 0.1%
44 4
< 0.1%
45 3
< 0.1%
ValueCountFrequency (%)
94 1
 
< 0.1%
93 16
 
0.2%
92 34
 
0.4%
91 103
1.1%
90 100
 
1.0%
89 160
1.7%
88 200
2.1%
87 190
2.0%
86 253
2.6%
85 223
2.3%

MSinceMostRecentTradeOpen
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct104
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.287957
Minimum0
Maximum227
Zeros105
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:07.896406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q311
95-th percentile28
Maximum227
Range227
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.727606
Coefficient of variation (CV)1.262668
Kurtosis52.992534
Mean9.287957
Median Absolute Deviation (MAD)4
Skewness5.2940624
Sum89926
Variance137.53674
MonotonicityNot monotonic
2024-05-31T11:32:08.507837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1152
11.9%
3 1004
 
10.4%
4 876
 
9.0%
5 747
 
7.7%
1 731
 
7.6%
6 652
 
6.7%
7 569
 
5.9%
8 458
 
4.7%
9 405
 
4.2%
10 304
 
3.1%
Other values (94) 2784
28.8%
ValueCountFrequency (%)
0 105
 
1.1%
1 731
7.6%
2 1152
11.9%
3 1004
10.4%
4 876
9.0%
5 747
7.7%
6 652
6.7%
7 569
5.9%
8 458
 
4.7%
9 405
 
4.2%
ValueCountFrequency (%)
227 1
< 0.1%
207 1
< 0.1%
184 1
< 0.1%
178 1
< 0.1%
163 1
< 0.1%
156 1
< 0.1%
152 1
< 0.1%
143 2
< 0.1%
121 1
< 0.1%
119 1
< 0.1%

AverageMInFile
Real number (ℝ)

Distinct235
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.109275
Minimum4
Maximum322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:09.012874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile29
Q158
median76
Q397
95-th percentile139
Maximum322
Range318
Interquartile range (IQR)39

Descriptive statistics

Standard deviation33.712984
Coefficient of variation (CV)0.42615716
Kurtosis2.194652
Mean79.109275
Median Absolute Deviation (MAD)19
Skewness0.88188721
Sum765936
Variance1136.5653
MonotonicityNot monotonic
2024-05-31T11:32:09.527141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 154
 
1.6%
71 153
 
1.6%
74 146
 
1.5%
80 145
 
1.5%
68 145
 
1.5%
75 144
 
1.5%
84 143
 
1.5%
69 139
 
1.4%
81 138
 
1.4%
70 138
 
1.4%
Other values (225) 8237
85.1%
ValueCountFrequency (%)
4 3
 
< 0.1%
5 6
 
0.1%
6 4
 
< 0.1%
7 7
0.1%
8 10
0.1%
9 13
0.1%
10 11
0.1%
11 8
0.1%
12 10
0.1%
13 16
0.2%
ValueCountFrequency (%)
322 1
< 0.1%
275 1
< 0.1%
273 1
< 0.1%
263 1
< 0.1%
258 1
< 0.1%
257 1
< 0.1%
256 1
< 0.1%
252 1
< 0.1%
249 1
< 0.1%
245 1
< 0.1%

NumSatisfactoryTrades
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.426048
Minimum0
Maximum79
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:10.530377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median20
Q328
95-th percentile42
Maximum79
Range79
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.178171
Coefficient of variation (CV)0.52170941
Kurtosis0.87911936
Mean21.426048
Median Absolute Deviation (MAD)7
Skewness0.82504621
Sum207447
Variance124.95151
MonotonicityNot monotonic
2024-05-31T11:32:11.171898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 384
 
4.0%
15 384
 
4.0%
16 376
 
3.9%
22 367
 
3.8%
19 365
 
3.8%
13 360
 
3.7%
21 346
 
3.6%
14 345
 
3.6%
20 342
 
3.5%
17 335
 
3.5%
Other values (63) 6078
62.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 20
 
0.2%
2 56
 
0.6%
3 73
 
0.8%
4 124
1.3%
5 143
1.5%
6 180
1.9%
7 201
2.1%
8 210
2.2%
9 249
2.6%
ValueCountFrequency (%)
79 1
 
< 0.1%
78 1
 
< 0.1%
75 1
 
< 0.1%
74 1
 
< 0.1%
70 2
 
< 0.1%
67 3
< 0.1%
66 2
 
< 0.1%
65 4
< 0.1%
64 6
0.1%
63 5
0.1%

NumTrades60Ever/DerogPubRec
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5743648
Minimum0
Maximum19
Zeros6706
Zeros (%)69.3%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:11.655242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.236724
Coefficient of variation (CV)2.153203
Kurtosis29.315425
Mean0.5743648
Median Absolute Deviation (MAD)0
Skewness4.2301312
Sum5561
Variance1.5294863
MonotonicityNot monotonic
2024-05-31T11:32:12.059746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 6706
69.3%
1 1758
 
18.2%
2 661
 
6.8%
3 237
 
2.4%
4 145
 
1.5%
5 69
 
0.7%
6 36
 
0.4%
7 29
 
0.3%
8 11
 
0.1%
10 8
 
0.1%
Other values (8) 22
 
0.2%
ValueCountFrequency (%)
0 6706
69.3%
1 1758
 
18.2%
2 661
 
6.8%
3 237
 
2.4%
4 145
 
1.5%
5 69
 
0.7%
6 36
 
0.4%
7 29
 
0.3%
8 11
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
12 3
 
< 0.1%
11 4
 
< 0.1%
10 8
0.1%
9 8
0.1%
8 11
0.1%

NumTrades90Ever/DerogPubRec
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37688494
Minimum0
Maximum19
Zeros7528
Zeros (%)77.8%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:12.429590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98651934
Coefficient of variation (CV)2.6175611
Kurtosis53.650237
Mean0.37688494
Median Absolute Deviation (MAD)0
Skewness5.5890429
Sum3649
Variance0.97322042
MonotonicityNot monotonic
2024-05-31T11:32:12.844659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 7528
77.8%
1 1398
 
14.4%
2 463
 
4.8%
3 123
 
1.3%
4 80
 
0.8%
5 33
 
0.3%
6 17
 
0.2%
7 16
 
0.2%
9 8
 
0.1%
10 5
 
0.1%
Other values (6) 11
 
0.1%
ValueCountFrequency (%)
0 7528
77.8%
1 1398
 
14.4%
2 463
 
4.8%
3 123
 
1.3%
4 80
 
0.8%
5 33
 
0.3%
6 17
 
0.2%
7 16
 
0.2%
8 3
 
< 0.1%
9 8
 
0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
16 2
 
< 0.1%
14 1
 
< 0.1%
12 2
 
< 0.1%
11 2
 
< 0.1%
10 5
 
0.1%
9 8
0.1%
8 3
 
< 0.1%
7 16
0.2%
6 17
0.2%

PercentTradesNeverDelq
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.545342
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:13.237485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69
Q189
median97
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)11

Descriptive statistics

Standard deviation11.327507
Coefficient of variation (CV)0.12239954
Kurtosis6.6924901
Mean92.545342
Median Absolute Deviation (MAD)3
Skewness-2.3042416
Sum896024
Variance128.31243
MonotonicityNot monotonic
2024-05-31T11:32:13.687018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 4511
46.6%
96 414
 
4.3%
97 389
 
4.0%
95 377
 
3.9%
94 351
 
3.6%
93 304
 
3.1%
92 292
 
3.0%
88 240
 
2.5%
90 216
 
2.2%
89 200
 
2.1%
Other values (61) 2388
24.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
11 1
 
< 0.1%
20 3
< 0.1%
22 1
 
< 0.1%
25 4
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
29 4
< 0.1%
30 4
< 0.1%
31 3
< 0.1%
ValueCountFrequency (%)
100 4511
46.6%
99 5
 
0.1%
98 173
 
1.8%
97 389
 
4.0%
96 414
 
4.3%
95 377
 
3.9%
94 351
 
3.6%
93 304
 
3.1%
92 292
 
3.0%
91 186
 
1.9%

MaxDelq/PublicRecLast12M
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7580045
Minimum0
Maximum9
Zeros323
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:14.014671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median6
Q37
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6379198
Coefficient of variation (CV)0.28445962
Kurtosis3.1790593
Mean5.7580045
Median Absolute Deviation (MAD)1
Skewness-1.7727301
Sum55749
Variance2.6827812
MonotonicityNot monotonic
2024-05-31T11:32:14.293931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 4147
42.8%
6 3056
31.6%
4 1596
 
16.5%
0 323
 
3.3%
5 232
 
2.4%
3 222
 
2.3%
1 52
 
0.5%
2 52
 
0.5%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 323
 
3.3%
1 52
 
0.5%
2 52
 
0.5%
3 222
 
2.3%
4 1596
 
16.5%
5 232
 
2.4%
6 3056
31.6%
7 4147
42.8%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
7 4147
42.8%
6 3056
31.6%
5 232
 
2.4%
4 1596
 
16.5%
3 222
 
2.3%
2 52
 
0.5%
1 52
 
0.5%
0 323
 
3.3%

MaxDelqEver
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3795703
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:14.563235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q16
median6
Q38
95-th percentile8
Maximum8
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8403953
Coefficient of variation (CV)0.28848265
Kurtosis0.11145921
Mean6.3795703
Median Absolute Deviation (MAD)2
Skewness-0.98469612
Sum61767
Variance3.387055
MonotonicityNot monotonic
2024-05-31T11:32:14.859869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
8 4437
45.8%
6 2840
29.3%
5 950
 
9.8%
2 712
 
7.4%
4 312
 
3.2%
3 302
 
3.1%
7 129
 
1.3%
ValueCountFrequency (%)
2 712
 
7.4%
3 302
 
3.1%
4 312
 
3.2%
5 950
 
9.8%
6 2840
29.3%
7 129
 
1.3%
8 4437
45.8%
ValueCountFrequency (%)
8 4437
45.8%
7 129
 
1.3%
6 2840
29.3%
5 950
 
9.8%
4 312
 
3.2%
3 302
 
3.1%
2 712
 
7.4%

NumTotalTrades
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.932452
Minimum0
Maximum104
Zeros131
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:15.249405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q114
median21
Q330
95-th percentile46
Maximum104
Range104
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.892225
Coefficient of variation (CV)0.56218258
Kurtosis1.5274942
Mean22.932452
Median Absolute Deviation (MAD)8
Skewness0.94262895
Sum222032
Variance166.20946
MonotonicityNot monotonic
2024-05-31T11:32:15.690340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 355
 
3.7%
16 354
 
3.7%
17 345
 
3.6%
22 333
 
3.4%
20 327
 
3.4%
24 325
 
3.4%
21 320
 
3.3%
18 312
 
3.2%
23 311
 
3.2%
19 310
 
3.2%
Other values (77) 6390
66.0%
ValueCountFrequency (%)
0 131
1.4%
1 26
 
0.3%
2 51
 
0.5%
3 92
 
1.0%
4 105
1.1%
5 116
1.2%
6 137
1.4%
7 176
1.8%
8 176
1.8%
9 231
2.4%
ValueCountFrequency (%)
104 1
 
< 0.1%
100 1
 
< 0.1%
87 1
 
< 0.1%
86 1
 
< 0.1%
85 1
 
< 0.1%
84 1
 
< 0.1%
83 3
< 0.1%
82 2
< 0.1%
79 2
< 0.1%
78 2
< 0.1%

NumTradesOpeninLast12M
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8878331
Minimum0
Maximum19
Zeros2397
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:16.192914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile5
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8320164
Coefficient of variation (CV)0.97043347
Kurtosis4.2279293
Mean1.8878331
Median Absolute Deviation (MAD)1
Skewness1.5302521
Sum18278
Variance3.3562841
MonotonicityNot monotonic
2024-05-31T11:32:16.653620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 2477
25.6%
0 2397
24.8%
2 1950
20.1%
3 1296
13.4%
4 743
 
7.7%
5 365
 
3.8%
6 229
 
2.4%
7 108
 
1.1%
8 56
 
0.6%
9 24
 
0.2%
Other values (8) 37
 
0.4%
ValueCountFrequency (%)
0 2397
24.8%
1 2477
25.6%
2 1950
20.1%
3 1296
13.4%
4 743
 
7.7%
5 365
 
3.8%
6 229
 
2.4%
7 108
 
1.1%
8 56
 
0.6%
9 24
 
0.2%
ValueCountFrequency (%)
19 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
12 4
 
< 0.1%
11 8
 
0.1%
10 18
 
0.2%
9 24
0.2%
8 56
0.6%

PercentInstallTrades
Real number (ℝ)

ZEROS 

Distinct94
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.853646
Minimum0
Maximum94
Zeros186
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:17.145361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q121
median33
Q345
95-th percentile64
Maximum94
Range94
Interquartile range (IQR)24

Descriptive statistics

Standard deviation16.827315
Coefficient of variation (CV)0.49706063
Kurtosis-0.0612557
Mean33.853646
Median Absolute Deviation (MAD)12
Skewness0.43904903
Sum327771
Variance283.15852
MonotonicityNot monotonic
2024-05-31T11:32:17.583193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 446
 
4.6%
50 401
 
4.1%
29 332
 
3.4%
25 320
 
3.3%
38 274
 
2.8%
20 271
 
2.8%
36 247
 
2.6%
40 239
 
2.5%
27 226
 
2.3%
17 224
 
2.3%
Other values (84) 6702
69.2%
ValueCountFrequency (%)
0 186
1.9%
2 1
 
< 0.1%
3 12
 
0.1%
4 29
 
0.3%
5 31
 
0.3%
6 36
 
0.4%
7 57
 
0.6%
8 77
0.8%
9 83
0.9%
10 84
0.9%
ValueCountFrequency (%)
94 1
 
< 0.1%
93 1
 
< 0.1%
92 2
 
< 0.1%
91 2
 
< 0.1%
90 3
 
< 0.1%
89 4
< 0.1%
88 3
 
< 0.1%
87 3
 
< 0.1%
86 9
0.1%
85 6
0.1%

NumInqLast6M
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4570337
Minimum0
Maximum66
Zeros3838
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:17.927784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum66
Range66
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1329779
Coefficient of variation (CV)1.463918
Kurtosis118.69404
Mean1.4570337
Median Absolute Deviation (MAD)1
Skewness6.221707
Sum14107
Variance4.5495947
MonotonicityNot monotonic
2024-05-31T11:32:18.396574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 3838
39.6%
1 2498
25.8%
2 1487
 
15.4%
3 751
 
7.8%
4 480
 
5.0%
5 239
 
2.5%
6 134
 
1.4%
7 81
 
0.8%
8 61
 
0.6%
9 32
 
0.3%
Other values (16) 81
 
0.8%
ValueCountFrequency (%)
0 3838
39.6%
1 2498
25.8%
2 1487
 
15.4%
3 751
 
7.8%
4 480
 
5.0%
5 239
 
2.5%
6 134
 
1.4%
7 81
 
0.8%
8 61
 
0.6%
9 32
 
0.3%
ValueCountFrequency (%)
66 1
 
< 0.1%
46 1
 
< 0.1%
29 1
 
< 0.1%
24 2
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 3
< 0.1%
18 1
 
< 0.1%
17 2
< 0.1%
16 4
< 0.1%

NumInqLast6Mexcl7days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3985747
Minimum0
Maximum66
Zeros3986
Zeros (%)41.2%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:18.887049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum66
Range66
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0928973
Coefficient of variation (CV)1.4964501
Kurtosis128.06333
Mean1.3985747
Median Absolute Deviation (MAD)1
Skewness6.4871004
Sum13541
Variance4.380219
MonotonicityNot monotonic
2024-05-31T11:32:19.251786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 3986
41.2%
1 2483
25.6%
2 1431
 
14.8%
3 750
 
7.7%
4 444
 
4.6%
5 228
 
2.4%
6 127
 
1.3%
7 71
 
0.7%
8 59
 
0.6%
9 26
 
0.3%
Other values (15) 77
 
0.8%
ValueCountFrequency (%)
0 3986
41.2%
1 2483
25.6%
2 1431
 
14.8%
3 750
 
7.7%
4 444
 
4.6%
5 228
 
2.4%
6 127
 
1.3%
7 71
 
0.7%
8 59
 
0.6%
9 26
 
0.3%
ValueCountFrequency (%)
66 1
 
< 0.1%
46 1
 
< 0.1%
29 1
 
< 0.1%
24 2
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
< 0.1%
18 2
< 0.1%
16 4
< 0.1%
15 2
< 0.1%

NetFractionRevolvingBurden
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct126
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.862219
Minimum0
Maximum232
Zeros608
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:19.708664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median29
Q356
95-th percentile89
Maximum232
Range232
Interquartile range (IQR)47

Descriptive statistics

Standard deviation28.899643
Coefficient of variation (CV)0.82896741
Kurtosis-0.4760277
Mean34.862219
Median Absolute Deviation (MAD)23
Skewness0.62400043
Sum337536
Variance835.18936
MonotonicityNot monotonic
2024-05-31T11:32:20.137723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 608
 
6.3%
1 303
 
3.1%
2 274
 
2.8%
3 259
 
2.7%
4 225
 
2.3%
5 208
 
2.1%
6 198
 
2.0%
8 166
 
1.7%
9 151
 
1.6%
7 150
 
1.5%
Other values (116) 7140
73.7%
ValueCountFrequency (%)
0 608
6.3%
1 303
3.1%
2 274
2.8%
3 259
2.7%
4 225
 
2.3%
5 208
 
2.1%
6 198
 
2.0%
7 150
 
1.5%
8 166
 
1.7%
9 151
 
1.6%
ValueCountFrequency (%)
232 1
< 0.1%
165 1
< 0.1%
154 1
< 0.1%
135 1
< 0.1%
131 1
< 0.1%
126 1
< 0.1%
124 1
< 0.1%
120 2
< 0.1%
118 2
< 0.1%
117 1
< 0.1%

NumRevolvingTradesWBalance
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1124768
Minimum0
Maximum32
Zeros295
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:20.503977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile10
Maximum32
Range32
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0203407
Coefficient of variation (CV)0.73443349
Kurtosis5.2551092
Mean4.1124768
Median Absolute Deviation (MAD)2
Skewness1.6947093
Sum39817
Variance9.1224577
MonotonicityNot monotonic
2024-05-31T11:32:20.901331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 1708
17.6%
3 1608
16.6%
4 1361
14.1%
1 1309
13.5%
5 1010
10.4%
6 743
7.7%
7 506
 
5.2%
8 350
 
3.6%
0 295
 
3.0%
9 242
 
2.5%
Other values (19) 550
 
5.7%
ValueCountFrequency (%)
0 295
 
3.0%
1 1309
13.5%
2 1708
17.6%
3 1608
16.6%
4 1361
14.1%
5 1010
10.4%
6 743
7.7%
7 506
 
5.2%
8 350
 
3.6%
9 242
 
2.5%
ValueCountFrequency (%)
32 1
 
< 0.1%
29 1
 
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 3
< 0.1%
23 1
 
< 0.1%
21 5
0.1%
20 4
< 0.1%
19 4
< 0.1%

PercentTradesWBalance
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.92326
Minimum0
Maximum100
Zeros43
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size75.8 KiB
2024-05-31T11:32:21.298734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q150
median67
Q383
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)33

Descriptive statistics

Standard deviation21.781379
Coefficient of variation (CV)0.33040506
Kurtosis-0.50328088
Mean65.92326
Median Absolute Deviation (MAD)17
Skewness-0.26415516
Sum638269
Variance474.42847
MonotonicityNot monotonic
2024-05-31T11:32:21.729309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1120
 
11.6%
50 731
 
7.6%
67 608
 
6.3%
75 533
 
5.5%
80 372
 
3.8%
60 342
 
3.5%
83 316
 
3.3%
71 281
 
2.9%
33 272
 
2.8%
57 245
 
2.5%
Other values (83) 4862
50.2%
ValueCountFrequency (%)
0 43
0.4%
4 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 3
 
< 0.1%
9 2
 
< 0.1%
10 6
 
0.1%
11 8
 
0.1%
12 3
 
< 0.1%
13 15
 
0.2%
ValueCountFrequency (%)
100 1120
11.6%
96 3
 
< 0.1%
95 13
 
0.1%
94 28
 
0.3%
93 37
 
0.4%
92 82
 
0.8%
91 82
 
0.8%
90 93
 
1.0%
89 138
 
1.4%
88 176
 
1.8%

Interactions

2024-05-31T11:31:59.837910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:45.796486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:52.516683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:58.689161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:04.543211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:11.049426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:17.096018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:23.209403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:28.829785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:35.022073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:39.589764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:42.693625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:45.520595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:48.453923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:51.285013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:54.126762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:57.018245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:32:00.029997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:46.144056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:52.882580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:59.048059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:04.891287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:11.397641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:17.438278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:23.526783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:29.145040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:35.455948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:39.811998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:42.929828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:45.687035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:48.631814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:51.444927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:54.280251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:57.183268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:32:00.192593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:46.500139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:53.195210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:59.344471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:05.207494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:11.728535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:17.783480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:23.838251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:29.447645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:35.771900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:40.023178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:43.080342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:46.053973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:48.779384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:51.605028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:54.414489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:57.326813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:32:00.358028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:46.861096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:53.566869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:59.685730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:05.560652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:12.101122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:18.143234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:24.143800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:29.863945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:36.114918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:40.216534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:43.248357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:46.211009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:48.954036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:51.780642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-31T11:31:48.128409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:50.955975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:53.792778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:56.677933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:59.517285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:32:03.516038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:52.127043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:30:58.320790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:04.230618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:10.338115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:16.743090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:22.765560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:28.515791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:34.706099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:39.330153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:42.540559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:45.373019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:48.286996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:51.112506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:53.964134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:56.847106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-31T11:31:59.688572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-31T11:32:22.054583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AverageMInFileExternalRiskEstimateMSinceMostRecentTradeOpenMaxDelq/PublicRecLast12MMaxDelqEverNetFractionRevolvingBurdenNumInqLast6MNumInqLast6Mexcl7daysNumRevolvingTradesWBalanceNumSatisfactoryTradesNumTotalTradesNumTrades60Ever/DerogPubRecNumTrades90Ever/DerogPubRecNumTradesOpeninLast12MPercentInstallTradesPercentTradesNeverDelqPercentTradesWBalanceRiskPerformance
AverageMInFile1.0000.3280.303-0.025-0.043-0.209-0.202-0.199-0.0010.1580.1280.013-0.012-0.385-0.158-0.041-0.1990.249
ExternalRiskEstimate0.3281.0000.1430.6220.533-0.640-0.231-0.233-0.3610.004-0.041-0.479-0.411-0.195-0.1500.588-0.4540.461
MSinceMostRecentTradeOpen0.3030.1431.000-0.008-0.001-0.052-0.336-0.342-0.209-0.216-0.2150.0170.005-0.750-0.023-0.050-0.0020.069
MaxDelq/PublicRecLast12M-0.0250.622-0.0081.0000.824-0.184-0.048-0.052-0.117-0.032-0.076-0.539-0.4240.006-0.1220.833-0.1480.278
MaxDelqEver-0.0430.533-0.0010.8241.000-0.155-0.042-0.043-0.0570.012-0.052-0.687-0.5200.013-0.1240.902-0.1270.240
NetFractionRevolvingBurden-0.209-0.640-0.052-0.184-0.1551.0000.0970.0980.411-0.036-0.0180.1220.0940.0780.132-0.1940.6130.349
NumInqLast6M-0.202-0.231-0.336-0.048-0.0420.0971.0000.9830.1060.1130.1180.0490.0570.3040.044-0.0220.0500.081
NumInqLast6Mexcl7days-0.199-0.233-0.342-0.052-0.0430.0980.9831.0000.1100.1170.1230.0490.0580.3060.044-0.0220.0510.077
NumRevolvingTradesWBalance-0.001-0.361-0.209-0.117-0.0570.4110.1060.1101.0000.4760.407-0.028-0.0450.268-0.244-0.0220.2930.134
NumSatisfactoryTrades0.1580.004-0.216-0.0320.012-0.0360.1130.1170.4761.0000.856-0.133-0.1290.290-0.0800.127-0.2220.140
NumTotalTrades0.128-0.041-0.215-0.076-0.052-0.0180.1180.1230.4070.8561.000-0.044-0.0550.267-0.0310.049-0.1720.093
NumTrades60Ever/DerogPubRec0.013-0.4790.017-0.539-0.6870.1220.0490.049-0.028-0.133-0.0441.0000.830-0.0330.120-0.5730.1370.153
NumTrades90Ever/DerogPubRec-0.012-0.4110.005-0.424-0.5200.0940.0570.058-0.045-0.129-0.0550.8301.000-0.0220.103-0.4070.1180.120
NumTradesOpeninLast12M-0.385-0.195-0.7500.0060.0130.0780.3040.3060.2680.2900.267-0.033-0.0221.0000.0260.0720.0140.082
PercentInstallTrades-0.158-0.150-0.023-0.122-0.1240.1320.0440.044-0.244-0.080-0.0310.1200.1030.0261.000-0.1330.3860.137
PercentTradesNeverDelq-0.0410.588-0.0500.8330.902-0.194-0.022-0.022-0.0220.1270.049-0.573-0.4070.072-0.1331.000-0.1740.257
PercentTradesWBalance-0.199-0.454-0.002-0.148-0.1270.6130.0500.0510.293-0.222-0.1720.1370.1180.0140.386-0.1741.0000.274
RiskPerformance0.2490.4610.0690.2780.2400.3490.0810.0770.1340.1400.0930.1530.1200.0820.1370.2570.2741.000

Missing values

2024-05-31T11:32:04.172757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-31T11:32:05.317553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RiskPerformanceExternalRiskEstimateMSinceMostRecentTradeOpenAverageMInFileNumSatisfactoryTradesNumTrades60Ever/DerogPubRecNumTrades90Ever/DerogPubRecPercentTradesNeverDelqMaxDelq/PublicRecLast12MMaxDelqEverNumTotalTradesNumTradesOpeninLast12MPercentInstallTradesNumInqLast6MNumInqLast6Mexcl7daysNetFractionRevolvingBurdenNumRevolvingTradesWBalancePercentTradesWBalance
0Bad55.04.084.020.03.00.083.03.05.023.01.043.00.00.033.08.069.0
1Bad61.015.041.02.04.04.0100.00.08.07.00.067.00.00.00.00.00.0
2Bad67.05.024.09.00.00.0100.07.08.09.04.044.04.04.053.04.086.0
3Bad66.01.073.028.01.01.093.06.06.030.03.057.05.04.072.06.091.0
4Bad81.027.0132.012.00.00.0100.07.08.012.00.025.01.01.051.03.080.0
5Bad59.011.078.031.00.00.091.04.06.032.01.047.00.00.062.012.094.0
6Good54.07.037.025.00.00.092.04.06.026.03.058.04.04.089.07.0100.0
7Good68.07.065.017.00.00.083.06.06.018.01.044.00.00.028.02.040.0
8Bad59.02.0138.024.00.00.085.04.06.027.01.026.01.01.068.07.090.0
9Bad61.04.036.019.00.00.095.04.06.019.03.026.06.06.031.05.062.0
RiskPerformanceExternalRiskEstimateMSinceMostRecentTradeOpenAverageMInFileNumSatisfactoryTradesNumTrades60Ever/DerogPubRecNumTrades90Ever/DerogPubRecPercentTradesNeverDelqMaxDelq/PublicRecLast12MMaxDelqEverNumTotalTradesNumTradesOpeninLast12MPercentInstallTradesNumInqLast6MNumInqLast6Mexcl7daysNetFractionRevolvingBurdenNumRevolvingTradesWBalancePercentTradesWBalance
9672Good57.04.087.018.00.00.089.04.06.018.01.056.01.01.093.05.0100.0
9673Good84.06.0103.025.00.00.0100.07.08.025.02.024.00.00.010.04.070.0
9674Good79.02.068.027.00.00.0100.07.08.030.03.036.04.04.01.04.060.0
9675Good90.05.092.013.00.00.0100.07.08.013.01.046.02.02.05.03.0100.0
9676Bad75.02.0121.037.01.01.083.04.03.043.01.07.01.01.034.07.053.0
9677Good73.05.057.021.00.00.095.06.06.021.05.019.00.00.026.05.0100.0
9678Bad65.039.068.011.00.00.092.06.06.012.00.042.01.01.086.02.080.0
9679Bad74.06.064.018.01.01.0100.06.08.018.01.033.04.04.06.05.056.0
9680Bad72.012.0113.042.02.02.096.06.02.045.00.020.00.00.019.04.038.0
9681Bad66.01.017.04.00.00.0100.07.08.05.02.060.03.02.067.02.0100.0

Duplicate rows

Most frequently occurring

RiskPerformanceExternalRiskEstimateMSinceMostRecentTradeOpenAverageMInFileNumSatisfactoryTradesNumTrades60Ever/DerogPubRecNumTrades90Ever/DerogPubRecPercentTradesNeverDelqMaxDelq/PublicRecLast12MMaxDelqEverNumTotalTradesNumTradesOpeninLast12MPercentInstallTradesNumInqLast6MNumInqLast6Mexcl7daysNetFractionRevolvingBurdenNumRevolvingTradesWBalancePercentTradesWBalance# duplicates
0Bad62.09.042.010.02.01.083.03.05.012.02.033.01.01.014.04.063.02
1Bad64.018.0124.016.02.00.047.04.05.019.00.074.04.04.027.02.0100.02